In order to get disentangled representation, some previous work has made a significant contribution to it. Based on VAE, \(\beta \) -VAE [1]} adds a coefficient weight to the KL-divergence term of the VAE loss and get a more disentangled representation. Mostly there is a significant advantage in that it trains more stably than InfoGAN. However, \(\beta \) -VAE sacrifices the reconstruction result at the same time. \(\beta \) -TCVAE [2]} and FactorVAE [3]} explored this issue in more detail and found TC term is the immediate causes to promote disentanglement.
